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Senior Inference Optimization Engineer - Dragonfly Portfolio at Dragonfly · Katori

Asks for

The ad says“Proficiency in Python, Rust, or Go. C++/CUDA a strong plus”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“Proficiency in Python, Rust, or Go. C++/CUDA a strong plus”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“Proficiency in Python, Rust, or Go. C++/CUDA a strong plus”
The ad says“5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge”
The ad says“5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“Hands-on experience with at least one production LLM inference engine (vLLM, SGLang) running at high volume”
The ad says“Experience with distributed inference strategies: tensor parallelism, pipeline parallelism, MoE parallelism in multi-GPU and multi-node environments”
The ad says“GPU profiling fluency: Nsight Systems, Nsight Compute, PyTorch Profiler”
The ad says“GPU profiling fluency: Nsight Systems, Nsight Compute, PyTorch Profiler”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge”
The ad says“5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge”
The ad says“Experience with distributed inference strategies: tensor parallelism, pipeline parallelism, MoE parallelism in multi-GPU and multi-node environments”
The ad says“Proficiency in Python, Rust, or Go. C++/CUDA a strong plus”
The ad says“GPU profiling fluency: Nsight Systems, Nsight Compute, PyTorch Profiler”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“Proficiency in Python, Rust, or Go. C++/CUDA a strong plus”
The ad says“Hands-on experience with at least one production LLM inference engine (vLLM, SGLang) running at high volume”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“Experience with distributed inference strategies: tensor parallelism, pipeline parallelism, MoE parallelism in multi-GPU and multi-node environments”
The ad says“Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile”
The ad says“Hands-on experience with at least one production LLM inference engine (vLLM, SGLang) running at high volume”

Dragonfly is a crypto-native Venture Capital and research firm with $3.6B+ in assets under management and 160+ portfolio companies. Our Talent team connects people with roles across our portfolio, opening the door to opportunities through our Talent Network.

This is an application to join our talent network. This is not a listing for an internal role at Dragonfly.

We're actively sourcing for a Senior Inference Optimization Engineer for one of our portfolio companies building privacy-first consumer AI infrastructure. You'll be on the bleeding edge of LLM inference performance, pushing throughput, driving down latency, and optimizing cost per token at significant scale.

Location: Remote, USA (open to excellent candidates outside the USA)

What We’re Looking For:

5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge
Hands-on experience with at least one production LLM inference engine (vLLM, SGLang) running at high volume
Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile
Experience with distributed inference strategies: tensor parallelism, pipeline parallelism, MoE parallelism in multi-GPU and multi-node environments
GPU profiling fluency: Nsight Systems, Nsight Compute, PyTorch Profiler
Proficiency in Python, Rust, or Go. C++/CUDA a strong plus
Bonus: custom Triton kernels, diffusion/image model inference optimization, open-source inference framework contributions

About the role:

Stand up and optimize GPU infrastructure including B300 nodes in owned data centers
Drive down TTFT and TPOT, push throughput, and improve cost per token for LLM inference workloads
Build reproducible benchmarking harnesses across inference engines to identify optimal engine, quantization scheme, and parallelism strategy per workload and GPU SKU
Optimize multivariate inference load-balancing algorithms within the inference routing system
Evaluate emerging inference optimization techniques including custom CUDA/Triton kernels, novel attention variants, new quantization schemes, and compilation stack improvements
Evaluate emerging inference hardware (FPGAs, ASICs, custom silicon) for viability in the stack

Even if you don't match every point above but are an engineer passionate about AI and/or crypto, we encourage you to apply. There may be other opportunities that fit your skill set.

Process:

We'll review your application and assess fit for this role.
If there's a match, we'll facilitate a warm introduction to the team.
If the timing isn't right, we'll keep you in mind for future opportunities across the portfolio.

Compensation

Early Career: $110,000–$150,000
Mid: $150,000–$200,000
Senior: $180,000–$250,000
Staff/Leadership: $230,000–$330,000

The compensation range reflects variation by seniority, location, and hiring company. Final compensation is confirmed with the specific hiring company.

Submit your information below, and we’ll reach out if there’s a potential fit.

Asks for

Values